1. Field of the Invention
A statistical method for modeling a primary subsurface petrophysical variable from a study of sparsely-distributed primary data in combination with closely-spaced secondary measurements.
2. Discussion of Related Art
During the course of seismic exploration, an acoustic wavefield is generated at or near the surface of the earth to insonify the underlying earth layers or strata. The wavefield is reflected in turn from each subsurface stratum whence the wavefield returns to the surface. The returning reflected wavefield manifests itself as a periodic mechanical motion of the earth's surface that is detected by suitable sensors. The sensors convert the mechanical motion to electrical signals which are recorded on an archival storage medium such as time-scale recordings in analog or digital format as desired by the investigator.
Quantities of interest include reflection travel time and reflection amplitude. Reflection travel time is a measure of the depths of the respective strata. Reflection amplitude depends upon the reflectivity coefficient at the interface between two strata. The reflectivity coefficient depends upon the difference in acoustic impedance across the interface. The acoustic impedance of a stratum is defined by the product of the acoustic velocity and the density of that rock layer. Acoustic impedance is measured in units of meters per second per gram per cubic centimeter.
To be of use quantitatively, the observed reflection amplitude must be corrected for spherical spreading, instrumental artifacts and other predictable effects to provide true amplitude measurements. The resulting true amplitudes may be used to calculate the acoustic impedances of the respective strata. A change in acoustic impedance may be a parameter indicative of a change in rock type.
In the course of geoexploration, control points may be established by boreholes, often quite widely separated, that penetrate strata of interest. At such sparse control points one can make actual measurements of selected petrophysical variables which constitute measurements of a primary variable. Collocated and preferably concurrent measurements of a selected seismic attribute comprise measurements of a secondary variable that may be calibrated with respect to the primary measurements.
The desideratum of a seismic survey line, having relatively closely-spaced observation stations that are distributed between the sparse control points, is to estimate the continuity and distribution of a primary petrophysical variable on the basis of measurements of a secondary variable based on the seismic data. Although seismic measurements are preferred because of their greater resolution, measurements of other geophysical quantities including potential field data may also be used.
By way of definition, in this disclosure, the terms "station", "point", "observation point", "observation station", "measurement point" are synonymous. The term "pixel" refers to a virtual or real pictorial element representative of the mapped location of a measurement station. A "control point" is a location whereat an actual measurement is made of a selected petrophysical variable.
U.S. Pat. No. 4,926,394 issued May 15, 1990 to Phillipe M. Doyen and assigned to the assignee of this invention, teaches a type of Monte Carlo statistical method for estimating the variation in rock type or texture, that is, the change in lithology along a given stratum or a related grouping of strata within a selected geologic formation. The estimates are based on seismic data gathered over an array of survey lines that coincide with sparsely-spaced control points such as boreholes. This is a type of maximum a posteriori estimation technique. It suffers from the disadvantages that a) it is computer intensive; b) it is sometimes difficult to guarantee convergence of the iterative optimization procedure used in the technique; c) it is difficult to specify the required lithology transition statistics.
In a paper by Hua Zhou et al. entitled Formatting and Integrating Soft Data: Stochastic Imaging via the Markov-Bayes Algorithm, published in Geostatics Troia, 92, Kluwer Publishers, encodes, under a Bayesian framework, local prior probability distributions from both hard data, that is, primary data derived from actual measurements of a desired variable at control points such as boreholes, and soft data derived from measurements of an associated secondary variable such as seismic data. The local prior distributions of a petrophysical property are then updated into posterior distributions using nearby hard and soft data. The posterior distributions provide models of uncertainty prevailing at sampled locations. The Markov-Bayes algorithm for such updating can be seen as cokriging capitalizing of spatial correlations between prior distribution values. A Markov-type assumption stating that the hard data prevail over collocated soft data, allows determination of the hard/soft data coregionalization model through some simple calibration parameters.
Paper SPE 24742 entitled Integrating Seismic Data in Reservoir Modeling: The collocated Cokriging Alternative, written by W. Xu et al, delivered at the 1992 SPE Annual Technical Conference, explains that two sources of information commonly available for modeling the top of a structure: 1) Depth data from wells and 2) Geophysical measurements from seismic surveys, are often difficult to integrate. They teach use of geostatistical methods such as collocated cokriging to integrate the accurate but sparse well measurements with the generally less precise but more abundant seismic measurements.
Another paper of interest entitled Geophysical-hydrological Identification of Field Permeabilities Through Bayesian Updating, by Nadim Copty et al. is published in Water Resources Research, v. 29, n. 8, pp. 2813-2825, August, 1993. Here is presented a Bayesian method to identify the spatial distribution of water-reservoir permeability. The approach incorporates densely-spaced seismic velocity measurements with sparsely-sampled permeability and pressure data. The two classes of data exhibit a semi-empirical relationship which is used to update the data in the Bayesian sense.
U.S. Pat. No. 5,416,750 issued May 16, 1995 to Phillipe Doyen et al. assigned to the assignee of this invention and incorporated herein by reference, teaches simulation of a discretized lithologic model of the subsurface that is defined by a regular array of pixels. Each pixel corresponds to one of a finite number of possible lithoclasses such as sand, shale or dolomite. The lithoclasses are unknown except at a small number of sparsely distributed control pixels associated with borehole locations. Associated with each pixel there is a multivariate record of seismic attributes that may be statistically correlatable with the local lithology. A Monte Carlo method is used to simulate the lithoclass spatial distribution by combining the lithologic data at control pixels with data records of seismic attributes. Using Indicator Kriging, a prior probability distribution of the lithoclasses is calculated for each pixel from the lithology values at neighboring pixels. The likelihood of each lithoclass is also calculated in each pixel from the corresponding conditional probability distribution of seismic attributes. A posterior lithoclass probability distribution is obtained at each pixel by multiplying the prior distribution and the likelihood function. The posterior distributions are sampled pixel-by-pixel to generate equally probable models of the subsurface lithology.
The method outlined in the '750 patent is suitable for use with lithology wherein the variation in a rock property may be defined in terms of discrete classes such as sand or shale or limestone. Other rock properties such as permeability or porosity may vary continuously rather than discretely between control points. This invention proposes a new multivariate stochastic method which can be applied when the relationship between the simulated continuous primary variable and one or several collocated secondary variables is non linear.